1. Field of the Invention
This invention relates to the field of data compression.
2. Description of the Related Art
With the explosion of the digital age, there has been a tremendous increase in the amount of data being transmitted from one point to another. Data may be traveling within an office, within a country, from one country to another, from Earth to locations in outer space, or from locations in outer space back to Earth.
Increasingly capable instruments and ever more ambitious scientific objectives produce ever-greater data flows to scientists, and this is particularly true for scientific missions involving spacecraft. A large variety of compression algorithms have been developed for imaging data, yet little attention has been given to the large amount of data acquired by many other types of instruments. In particular, radar sounders, radar synthetic aperture mappers, mass spectrometers, and other such instruments have become increasingly important to space missions. Although the volume of scientific data obtained has grown with the increasing sophistication of the instruments used to obtain the data, spacecraft capabilities for telecommunications bandwidth have not grown at the same rate. The tightening constraints on spacecraft cost, mass, power, and size, limit the amount of resources that can be devoted to relaying the science data from the spacecraft to the ground. Competition for use of ground station resources, such as the NASA Deep Space Network, further limit the number of bits that can be transmitted to Earth.
One approach to increase the “scientific return” in the face of these constraints is to use data compression, as has been adopted for many NASA scientific missions. For example, the Galileo mission to explore the planet Jupiter and its moons has made extensive use of lossy image compression methods, such as the discrete cosine transform, after the high gain antenna of the Galileo spacecraft failed to deploy properly. By compressing the data, the Galileo team was able to capture data using the spacecraft's smaller, properly functioning antenna.
Other missions, like NEAR, make routine use of both lossless and lossy image compression to reduce data volume, employing several different algorithms. In both the NEAR and Galileo programs, scientists felt that the inevitable loss of information associated with data compression and decompression was more than compensated by the opportunity to return more measurements, that is, there is net scientific gain when more measurements are returned (or higher temporal/spatial/spectral resolution is achieved), even with loss of fidelity of data returned.
Standard image compression methods like discrete cosine transforms and related methods are optimized for image data and are not easily adaptable to the data streams from non-image sources (e.g., a spectrometer) or to time series data sources (those with a time component, such as video), and their performance characteristics (in terms of what information is lost by compression) are not necessarily optimal for such time series data. One reason for this is that image compression methods take advantage of 2-dimensional spatial correlations generally present in images, but such correlations are absent or qualitatively different in time-series data, such as data from a spectrometer or particle/photon counter. However, the need for compression of non-image data is growing and will continue to grow in the future. For example, hyper-spectral images from a scanning spectrograph are particularly high bandwidth but not suited for compression by standard techniques. Further, lossless compression methods such as Huffman encoding, run-length encoding, and Fast and Rico algorithms, and lossy methods such as straight quantization, provide relatively small compression rates. Thus, it would be desirable to significantly increase the time resolution of such an instrument within the bandwidth allocation currently available, and increase the compression ratio available when compressing this data, while still retaining the scientific value of the compressed data, and while being able to use the same compression method for single or multi-dimensional applications.